This study presents a framework for assessing the scalability of innovations aimed at achieving low-emission development, using a case study of Nandi County, Kenya. Food system emissions make up a significant portion of Kenya’s total greenhouse gas emissions. Reducing these emissions is crucial for lowering the country’s overall emissions, supporting Kenya’s commitment to the Paris Agreement. To achieve this, Kenya must scale innovations that can transform the food system toward low emissions while meeting national development targets. The framework was thus tested, in the scope of two participatory workshops, on three CGIAR innovations with relevance to Nandi County: Improved Livestock Breeds and Feeds (ILBF), Integrated Aquaculture Practices (IAP), and Biogas Technology (BT). Stakeholders evaluated these innovations in a two-day participatory workshop using a multi-criteria scoring process. Based on established criteria, the participants developed scores that reflected an innovations’ potential for scaling. ILBF scored 24 out of 33 points (75%), IAP scored 27 (81%), and BT scored 26 (76%). The results indicate varying potential for scaling among the innovations, highlighting the importance of context-specific and systemic enabling factors, beyond technical performance. These include institutional, social, and biophysical contexts, adoption barriers, market potential, costs of innovation, financial opportunities for scaling, environmental and social co-benefits and trade-offs as well as economic benefits, all of which are critical in creating an enabling environment for scaling. The framework serves as a support tool for evaluating potential opportunities, barriers, bottlenecks, and trade-offs to inform scaling strategies in a more systematic and responsible manner. Beyond its empirical findings, the study contributes methodologically by illustrating how participatory multi-criteria assessment can strengthen strategic decision-making for Agricultural Research for Development programs.

Members of the majority party in Congress sometimes vote against bills that they prefer over the status quo. We estimate a model of congressional roll-call voting that allows for this kind of non-ideological protest voting. We find that protest voting has significant implications for roll-call-based estimates of ideology and other analyses that rely upon them. For example, a traditional item response theory model curiously identifies members of the Squad as relatively moderate Democrats, but our protest-voting-adjusted scores identify them as the most liberal members of Congress. We also find that previous studies may have underestimated responsiveness, the effects of ideology in elections, the utility of non-roll-call-based measures of ideology, and the increase in congressional polarization. Although the implications for most substantive applications are likely modest, our analyses suggest that future researchers can better measure legislative ideology by accounting for a small number of non-ideological votes.

Populists believe in the sovereignty of the people. Yet, the people can be construed in ethnic terms or in civic terms. Using a novel wording experiment in Germany (N = 7,034), we examine whether ethnic or civic conceptions of ‘the people’ affect respondents' adherence to key populist attitude items. ‘Volk’ in German has an ethno‐nationalist connotation and has been used throughout history to signify ‘ethnic Germans’. The concept ‘Bürger’, or citizens, by contrast, lacks an ethnic undercurrent. Similar ethnic connotations of ‘the people’ are also common in other languages. We find that there are statistically significant differences between items framed in an ethnic and a civic manner – and that this differs per item. This relationship is significantly moderated by respondents' degree of exclusive national identity and voting behaviour for the radical right. Our findings suggest that the way in which the people is conceptualized has important implications for the measurement of populist attitudes. When populist attitudes are measured with an ethnic understanding of the ‘the people’, the construct is biased towards right‐wing populism, inhibiting the measurement of populism as a ‘thin ideology’. Moreover, we demonstrate the importance of careful translations in comparative research, since some translated synonyms carry different semantic meanings and thereby change the concept under investigation.

Social and sustainable initiatives generally start small and need to scale to create substantial impact. Our systematic review of 133 articles develops a better understanding of this scaling process. From the literature, we conceptualize impact as the result of two different pathways: ‘scaling out’ (extending geographical space or volume) and ‘scaling up’ (influence on public discourses, political agendas and legislation). The review identified strategy, actor characteristics and institutional environment as key factors for scaling. The literature indicates that for strategy a focus on open structures generates speed and higher impact, but we also found critical views on this. The literature shows that the actor characteristics such as the ambition to scale, equal focus on the economic and the social logic, entrepreneurial skills and leadership are positively related to the level of impact. The institutional environment influences actor characteristics and strategy choices and also has a direct effect on the level of social and sustainable impact.

Arcade video games evolved in a constrained design space, following patterns of diversification, stabilisation, and collapse that mirror macroevolutionary processes. Despite their historical significance and detailed digital records, arcade games remain underexplored in cultural evolution research. Drawing on a dataset of 7,205 machines spanning four decades, we reconstruct the evolutionary trajectories of arcade niches using a multi-scale framework that integrates trait-level innovation, genre-level selection, and systemic constraints. We identify two contrasting dynamics: (1) resilient genres—such as Fighter and Driving—maintained long-term viability through innovation and collaboration networks, while (2) early Maze and Shooter subgenres collapsed due to imitation and weak collaboration. Morphospace analysis reveals how technological traits—specifically CPU speed and ROM size—co-evolved with gameplay complexity, shaping the viable design space. We argue that genres operated as evolving cultural-ecological units—structured niches that shaped trait evolution through reinforcement, constraint, and feedback. This multi-scale perspective positions arcade games as a rich model system for studying cultural macroevolution.

With a novel experimental design we investigate whether risk perception, return expectations, and investment propensity are influenced by the scale of the vertical axis in charts. We explore this for two presentation formats, namely return charts and price charts, where we depict low- and high-volatility assets with distinct trends. We find that varying the scale strongly affects people’s risk perception, as a narrower scale of the vertical axis leads to significantly higher perceived riskiness of an asset even if the underlying volatility is the same. Furthermore, past returns predict future return expectations almost perfectly. In our setting perceived profitability was considered more important than perceived riskiness when making investment choices. Overall we show that adapting the scale of a chart makes it easier to recognize yearly return variations within a single security, but at the same time makes it harder to identify differences between dissimilar securities. This is something regulators should be aware of and take into account in the rules they set.

We use instruction-tuned large language models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.

This paper presents an overview of an approach to the quantitative analysis of qualitative data with theoretical and methodological explanations of the two cornerstones of the approach, Alternating Least Squares and Optimal Scaling. Using these two principles, my colleagues and I have extended a variety of analysis procedures originally proposed for quantitative (interval or ratio) data to qualitative (nominal or ordinal) data, including additivity analysis and analysis of variance; multiple and canonical regression; principal components; common factor and three mode factor analysis; and multidimensional scaling. The approach has two advantages: (a) If a least squares procedure is known for analyzing quantitative data, it can be extended to qualitative data; and (b) the resulting algorithm will be convergent. Three completely worked through examples of the additivity analysis procedure and the steps involved in the regression procedures are presented.

This paper studies the problem of scaling ordinal categorical data observed over two or more sets of categories measuring a single characteristic. Scaling is obtained by solving a constrained entropy model which finds the most probable values of the scales given the data. A Kullback-Leibler statistic is generated which operationalizes a measure for the strength of consistency among the sets of categories. A variety of data of two and three sets of categories are analyzed using the entropy approach.

Synthetic data are used to examine how well axiomatic and numerical conjoint measurement methods, individually and comparatively, recover simple polynomial generators in three dimensions. The study illustrates extensions of numerical conjoint measurement (NCM) to identify and model distributive and dual-distributive, in addition to the usual additive, data structures. It was found that while minimum STRESS was the criterion of fit, another statistic, predictive capability, provided a better diagnosis of the known generating model. That NCM methods were able to better identify generating models conflicts with Krantz and Tversky's assertion that, in general, the direct axiom tests provide a more powerful diagnostic test between alternative composition rules than does evaluation of numerical correspondence. For all methods, dual-distributive models are most difficult to recover, while consistent with past studies, the additive model is the most robust of the fitted models.

When the three-parameter logistic model is applied to tests covering a broad range of difficulty, there frequently is an increase in mean item discrimination and a decrease in variance of item difficulties and traits as the tests become more difficult. To examine the hypothesis that this unexpected scale shrinkage effect occurs because the items increase in complexity as they increase in difficulty, an approximate relationship is derived between the unidimensional model used in data analysis and a multidimensional model hypothesized to be generating the item responses. Scale shrinkage is successfully predicted for several sets of simulated data.

A Monte Carlo study was conducted to investigate the ability of three estimation criteria to recover the parameters of Case V and Case III models from comparative judgment data. Significant differences in recovery are shown to exist.

A new family of indices was introduced earlier as a link between two approaches: One based on item response theory and the other on sample statistics. In this study, the statistical properties of these indices are investigated and then the relationships to Guttman Scales, and to item and person response curves are discussed. Further, these indices are standardized, and an example of their potential usefulness for diagnosing students' misconceptions is shown.

A modified version of a coordinate adjustment technique which permits the analysis of comparisons of psychological intervals for an unknown ordering of stimuli is described and compared to the original version and to TORSCA. For configurations with a large number of points, knowledge of the rank order of the stimuli does not improve the solution. For configurations with a small number of points, the performance of the new algorithm with an unknown ordering is equivalent to TORSCA.

A procedure for ordering object (stimulus) pairs based on individual preference ratings is described. The basic assumption is that individual responses are consistent with a nonmetric multidimensional unfolding model. The method requires data where a numerical response is independently generated for each individual-object pair. In conjunction with a nonmetric multidimensional scaling procedure, it provides a vehicle for recovering meaningful object configurations.

Two least squares procedures for symmetrization of a conditional proximity matrix are derived. The solutions provide multiplicative constants for scaling the rows or columns of the matrix to maximize symmetry. It is suggested that the symmetrization is applicable for the elimination of bias effects like response bias, or constraints on the marginal frequencies imposed by the experimental design, as in confusion matrices.

I develop a survey method for estimating social influence over individual political expression, by combining the content-richness of document scaling with the flexibility of survey research. I introduce the “What Would You Say?” question, which measures self-reported usage of political catchphrases in a hypothetical social context, which I manipulate in a between-subjects experiment. Using Wordsticks, an ordinal item response theory model inspired by Wordfish, I estimate each respondent’s lexical ideology and outspokenness, scaling their political lexicon in a two-dimensional space. I then identify self-censorship and preference falsification as causal effects of social context on respondents’ outspokenness and lexical ideology, respectively. This improves upon existing survey measures of political expression: it avoids conflating expressive behavior with populist attitudes, it defines preference falsification in terms of code-switching, and it moves beyond trait measures of self-censorship, to characterize relative shifts in the content of expression between different contexts. I validate the method and present experiments demonstrating its application to contemporary concerns about self-censorship and polarization, and I conclude by discussing its interpretation and future uses.